Optical Coherence Tomography (OCT) is a technique for performing high-resolution cross-sectional imaging that can provide images of tissue structure on the micron scale in situ and in real time (see for example Huang et al. “Optical Coherence Tomography” Science 254 (5035): 1178 1991). OCT is a method of interferometry that determines the scattering profile of a sample along the OCT beam. Each scattering profile in the depth direction (z) is called an axial scan, or A-scan. Cross-sectional images (B-scans), and by extension 3D volumes, are built up from many A-scans, with the OCT beam moved to a set of transverse (x and y) locations on the sample. One of the principle advantages of OCT is its ability to image the various layers of the retina of the eye. Technical improvements in this modality permit data sets to be obtained in very short times. Due to these high data rates, it behooves the computational power to keep up with the demands of the instrument and of the clinician or physician, who expect polished images or diagnostic metrics to be displayed instantaneously.
One of the important aspects of clinical diagnoses is that of the retinal nerve fiber layer (RNFL). This is the top layer of the retina seen in OCT imaging of the posterior segment of the eye. Embedded within the retina are the axons of the ganglion cells, residing in the layer just below the RNFL. These axons carry the visual signals (action potentials) to the lateral geniculate nucleus and thus the visual cortex. Impairment of either the ganglion cells themselves or their axons in the RNFL results in a diminution of sight, as would be the case with glaucoma, for example. It has been determined that the earliest observable defect as a result of glaucoma is the thinning of the RNFL (see for example Tan et al. 2008). Early detection is imperative if subsequent treatments are to possess any effectiveness.
A vital diagnostic in the ability to discern the presence or progression of glaucoma, is the thickness of the RNFL. A thinner than expected RNFL suggests the presence of disease. Discovery of thinned RNFLs can be accomplished via visual inspection of each and every OCT A-scan in an OCT dataset, which is naturally time consuming. (An OCT dataset is hereinafter defined to be an array of data from an OCT interferometer in any dimensional form. An image can be the same as or a modified version of a subset of a dataset.)
A substantially more effective approach is via automatic computational segmentation and analysis of the layer: with each slice, edges are detected and distances between these edges or boundaries can be measured. Similar types of analyses can be applied to the edges of the various retinal layers including but not limited to the internal limiting membrane (ILM), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), and the outer nuclear layer (ONL), external limiting membrane (ELM), and those layers therein below. A discussion of segmentation techniques may be found in US2008100612 (hereby incorporated by reference).
Other retinal characteristics where segmented OCT data can provide useful information include epiretinal membranes, retinal cysts, central serous retinopathy, macular lesions, drusen, macular degeneration, edema, and lesions, subretinal fluid, and intraretinal exudate (Coats' Disease) among others. In the case of subretinal fluid, it is associated with choroidal neovascularization and RPE elevation, which can and will upset the usefulness of many segmentation algorithms.
Segmentation Algorithms
With OCT B-scans, the various retinal depths (layers) in a slice are imaged. It is then imperative to have the ability to segregate the various layers of the retina in an automatic fashion and derive thickness measurements for at least the more vital layers. In order to perform this task, innumerable algorithms have been developed to segment the observations into layers. Clinical evaluation of areas such as the macula and the optic nerve head are likewise vital for similar as well as other reasons. Obviously, it is imperative that the results presented via these automated algorithmic approaches be reliable. Part of the ability in producing reliable results from segmentation algorithms, is the quality of the data input to the algorithm. Besides just plain low signal-to-noise of the data, other possible deficiencies affecting downstream ability to segment layers (hereinafter termed ‘segmentability’) include poor contact between layers, speckle noise, high-photon absorptivity from hemoglobin (thus creating signal-less shadows), low resolution data (thus blurring the boundaries between layers), eye motion, and saccades, to mention a few. Boundaries are normally found by threshold methodologies which in turn are dependent upon relative reflectances of the layers.
There are a multitude of algorithms to deal with the segmentation problem (see, e.g., DeBuc 2011 and the citations contained therein). Many of these algorithmic approaches deal with either the intensity data or a gradient image and usually assume some basic shape or characteristic. Early attempts used a one-dimensional convolution kernel. More exotic approaches involve optimal graph search methods, Markov random fields, pliable geometric models, graph partitioning, support vector machine (SVM), model-based guidance, neural networks, clustering algorithms (e.g., k-clustering), etc. A common approach has been edge-based detection, and in many cases using a version of the Canny edge-detection methodology. Others have used region-based constraints, which look for similar intensities within a region. Most of these algorithms require considerable computational resources.
Pre-Processing Strategies
Prior to analyses of images by segmentation algorithms, the images are usually pre-processed to improve the sensitivity of the detection algorithm used (e.g., edge or region) to the morphological characteristics of the region of interest. Most of these involve smoothing by various kernels or averaging and speckle noise reduction processing. Typical pre-processing steps could include median filtering: Herzog et al. (2004); mean filtering: Ishikawa (2005); nonlinear anisotropic filtering: Mujat et al. (2005); combination: Baroni et al. (2007); Low/High Pass filtering: Farsiu et al. (2008); wavelets: Quellec et al. (2010); and, fixed-pattern noise removal: Gotzinger et al. (2008). These processing techniques and others would be readily recognized by the ordinary skilled person the art as reducing the contribution of noise to the signal.
Assessing Data Quality
Many of these aforementioned pre-processing strategies will work at least moderately successfully if the data in a dataset are of a high quality: meaning, high signal-to noise-ratio (SNR), and no unexpected gross abnormalities, etc.
A measure of the quality of an image, independent of its morphological characteristics would provide an important metric to avoid analyzing poor data. The independence of the image quality metric from morphology is important otherwise a bias could be introduced as those images tagged as having a low value may be such due to intrinsic, disease-related deficiencies. In other words, diseased retinas could yield low quality metrics.
Several studies have reported on different morphological dependent metrics for evaluating the quality of OCT data including SS (signal-strength), SNR, analysis confidence, and signal deviation (see, e.g., Stein et al. 2005, Wu et al. 2007, and Liu et al. 2009).